Empirical mode decomposition as a tool to remove the function electrical stimulation artifact from surface...

Area of Science:

• Biomedical Engineering
• Signal Processing
• Neuroscience

Background:

• Electrical stimulation (ES) artifacts contaminate surface electromyography (EMG) signals, posing a challenge for accurate physiological measurements.
• The overlapping frequency spectra of ES artifacts and EMG signals complicate artifact removal.

Purpose of the Study:

• To investigate the efficacy of Empirical Mode Decomposition (EMD) for removing ES artifacts from surface EMG signals.
• To assess EMD's potential in isolating and separating ES interference from genuine EMG activity.

Main Methods:

• Empirical Mode Decomposition (EMD) was applied to decompose EMG signals containing ES artifacts.
• Simulated ES signals were added to voluntary EMG to test the EMD algorithm's performance.
• The EMD method was combined with the energy operator-Teager-Kaiser Energy Operator (TKEO) for enhanced signal representation.

Main Results:

• The EMD algorithm demonstrated considerable success in isolating EMG signals from ES artifacts.
• Combining EMD with TKEO improved the representation of the EMG signal.
• A limitation identified was the loss of some high-frequency data during the signal reconstruction process.

Conclusions:

• EMD is a promising method for mitigating ES artifacts in surface EMG.
• Further research is required to optimize EMD parameters for artifact removal, minimizing data loss and improving artifact-free EMG reconstruction.